diff --git a/train_model.py b/train_model.py index db58958d..1c8a06a1 100644 --- a/train_model.py +++ b/train_model.py @@ -9,13 +9,18 @@ from sklearn.metrics import accuracy_score, classification_report from thefuzz import fuzz from collections import Counter import logging +import sys + +# Importiere deine bestehenden Helfer +from google_sheet_handler import GoogleSheetHandler +from helpers import normalize_company_name # Logging Setup logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # --- Konfiguration --- GOLD_STANDARD_FILE = 'erweitertes_matching.csv' -CRM_ACCOUNTS_FILE = 'CRM_Accounts.csv' # Annahme: Du hast einen Export des CRM Sheets als CSV +CRM_SHEET_NAME = "CRM_Accounts" MODEL_OUTPUT_FILE = 'xgb_model.json' TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib' @@ -29,20 +34,11 @@ STOP_TOKENS_BASE = { } CITY_TOKENS = set() -# --- Hilfsfunktionen (aus dem Original-Skript übernommen) --- +# --- Hilfsfunktionen --- def _tokenize(s: str): if not s: return [] return re.split(r"[^a-z0-9äöüß]+", str(s).lower()) -def normalize_company_name(name: str): - if not isinstance(name, str): return '' - name = name.lower() - name = re.sub(r'\(.*?\)', '', name) - name = re.sub(r'\[.*?\]', '', name) - name = re.sub(r'[^a-z0-9äöüß\s]', ' ', name) - name = re.sub(r'\s+', ' ', name).strip() - return name - def clean_name_for_scoring(norm_name: str): if not norm_name: return "", set() tokens = [t for t in _tokenize(norm_name) if len(t) >= 3] @@ -67,9 +63,13 @@ def create_features(mrec: dict, crec: dict, term_weights: dict): features['fuzz_token_set_ratio'] = fuzz.token_set_ratio(clean1, clean2) features['fuzz_token_sort_ratio'] = fuzz.token_sort_ratio(clean1, clean2) - features['domain_match'] = 1 if mrec.get('CRM Website') and str(mrec.get('CRM Website')).strip() != '' and mrec.get('CRM Website') == crec.get('Kandidat Website') else 0 - features['city_match'] = 1 if mrec.get('CRM Ort') and str(mrec.get('CRM Ort')).strip() != '' and mrec.get('CRM Ort') == crec.get('Kandidat Ort') else 0 - features['country_match'] = 1 if mrec.get('CRM Land') and str(mrec.get('CRM Land')).strip() != '' and mrec.get('CRM Land') == crec.get('Kandidat Land') else 0 + # Normalisiere Domains für den Vergleich + domain1 = str(mrec.get('CRM Website', '')).lower().replace('www.', '').split('/')[0] + domain2 = str(crec.get('Kandidat Website', '')).lower().replace('www.', '').split('/')[0] + features['domain_match'] = 1 if domain1 and domain1 == domain2 else 0 + + features['city_match'] = 1 if mrec.get('CRM Ort') and mrec.get('CRM Ort') == crec.get('Kandidat Ort') else 0 + features['country_match'] = 1 if mrec.get('CRM Land') and mrec.get('CRM Land') == crec.get('Kandidat Land') else 0 features['country_mismatch'] = 1 if (mrec.get('CRM Land') and crec.get('Kandidat Land') and mrec.get('CRM Land') != crec.get('Kandidat Land')) else 0 overlapping_tokens = toks1 & toks2 @@ -88,63 +88,42 @@ def create_features(mrec: dict, crec: dict, term_weights: dict): if __name__ == "__main__": logging.info("Starte Trainingsprozess für Duplikats-Checker v5.0") - # 1. Daten laden try: - gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';') - # Lade CRM Daten, um Gewichte zu berechnen. - # Idealerweise wäre dies ein aktueller Export aus dem Google Sheet. - # Für die Simulation nehmen wir die Daten aus dem Gold-Standard. - # Besser: Lade hier alle 22.000 CRM Accounts. - # Annahme: Du hast einen Export als CRM_Accounts.csv im Ordner - try: - crm_df = pd.read_csv(CRM_ACCOUNTS_FILE, sep=',') # Passe Trennzeichen ggf. an - logging.info(f"{len(crm_df)} CRM Accounts geladen für die Gewichtsberechnung.") - except FileNotFoundError: - logging.warning(f"'{CRM_ACCOUNTS_FILE}' nicht gefunden. Verwende Daten aus '{GOLD_STANDARD_FILE}' für Gewichte.") - crm_df = gold_df.rename(columns={'Kandidat': 'CRM Name'}) + gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';', encoding='utf-8') + logging.info(f"{len(gold_df)} Zeilen aus Gold-Standard-Datei '{GOLD_STANDARD_FILE}' geladen.") + + logging.info("Verbinde mit Google Sheets, um CRM-Daten zu laden...") + sheet_handler = GoogleSheetHandler() + crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME) + logging.info(f"{len(crm_df)} CRM Accounts aus Google Sheets geladen.") except Exception as e: - logging.critical(f"Fehler beim Laden der CSV-Dateien: {e}") + logging.critical(f"Fehler beim Laden der Daten: {e}") sys.exit(1) - # 2. Daten normalisieren - for col in ['CRM Name', 'Kandidat']: - gold_df[f'normalized_{col}'] = gold_df[col].astype(str).apply(normalize_company_name) - for col in ['CRM Ort', 'Kandidat Ort', 'CRM Land', 'Kandidat Land']: - gold_df[col] = gold_df[col].astype(str).str.lower().str.strip() - crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name) - - # 3. Term Weights (TF-IDF) auf dem gesamten CRM-Datensatz berechnen + gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name) + gold_df['normalized_Kandidat'] = gold_df['Kandidat'].astype(str).apply(normalize_company_name) + for col in ['CRM Ort', 'Kandidat Ort', 'CRM Land', 'Kandidat Land']: + gold_df[col] = gold_df[col].astype(str).str.lower().str.strip().replace('nan', '') + term_weights = {token: math.log(len(crm_df) / (count + 1)) for token, count in Counter(t for n in crm_df['normalized_name'] for t in set(clean_name_for_scoring(n)[1])).items()} logging.info(f"{len(term_weights)} Wortgewichte berechnet.") - # 4. Feature-Tabelle und Labels erstellen logging.info("Erstelle Features für den Trainingsdatensatz...") features_list = [] labels = [] for _, row in gold_df.iterrows(): - mrec = { - 'normalized_name': row['normalized_CRM Name'], - 'CRM Website': row['CRM Website'], - 'CRM Ort': row['CRM Ort'], - 'CRM Land': row['CRM Land'] - } - crec = { - 'normalized_name': row['normalized_Kandidat'], - 'Kandidat Website': row['Kandidat Website'], - 'Kandidat Ort': row['Kandidat Ort'], - 'Kandidat Land': row['Kandidat Land'] - } - - # Nur Zeilen mit einem Kandidaten verarbeiten - if pd.notna(row['Kandidat']): + # Nur Zeilen mit einem Kandidaten und einem Best Match verarbeiten + if pd.notna(row['Kandidat']) and pd.notna(row['Best Match Option']): + mrec = row.to_dict() + crec = {'Kandidat Website': row['Kandidat Website'], 'Kandidat Ort': row['Kandidat Ort'], 'Kandidat Land': row['Kandidat Land']} + features = create_features(mrec, crec, term_weights) features_list.append(features) - # Label erstellen: 1 wenn der Kandidat dem Gold-Standard entspricht, sonst 0 - is_correct_match = 1 if row['Kandidat'] == row.get('Best Match Option', '') else 0 # Angenommen Spalte G heißt jetzt so + is_correct_match = 1 if row['Kandidat'] == row['Best Match Option'] else 0 labels.append(is_correct_match) X = pd.DataFrame(features_list) @@ -153,24 +132,21 @@ if __name__ == "__main__": logging.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen und {X.shape[1]} Features.") logging.info(f"Verteilung der Klassen: {Counter(y)}") - # 5. Modell trainieren logging.info("Trainiere das XGBoost-Modell...") X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) - model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss') + model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight= (len(y) - sum(y)) / sum(y)) model.fit(X_train, y_train) logging.info("Modell erfolgreich trainiert.") - # 6. Modell validieren y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) logging.info(f"\n--- Validierungsergebnis ---") logging.info(f"Genauigkeit auf Testdaten: {accuracy:.2%}") logging.info("Detaillierter Report:") - logging.info("\n" + classification_report(y_test, y_pred)) + logging.info("\n" + classification_report(y_test, y_pred, zero_division=0)) - # 7. Finales Modell und Gewichte speichern model.save_model(MODEL_OUTPUT_FILE) joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE) logging.info(f"Modell in '{MODEL_OUTPUT_FILE}' und Gewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' erfolgreich gespeichert.") \ No newline at end of file